Monday, July 27, 2015

Can Big Data Algorithms Tell Better Stories Than Humans?

In a recent Forbes article by Bernard Marr he addresses issues about "Big Data" and Algorithms. 

Bernard Marr is a best-selling business author, keynote speaker and leading business performance, analytics and data expert. His latest books are ‘Big Data‘ and ‘KPIs for Dummies‘.

Algorithms is not a word that most people understand, but suffice it to say it is a means of analyzing data and using it to make some conclusion.

The power of an algorithm is much overated and depends upon the  quality of the human who created it, and the human who reads it's results, uses human judgement and makes a decision.

Narrative Sciences is a company which developed 'Quill"

What if the computer algorithms could tell more compelling stories than health statisticians, journalists, writers or business analysts? Well, this is increasingly becoming a reality. A new generation of Big Data tools are being put to automate story telling.

The ideas behind this application of analytics were first put to use generating automated news reports, covering sports and financial stories. Take the recent Wimbledon tennis championships as an example. The Slamtracker system developed by IBM IBM -1.22%monitors each game using sensors and cameras, generating millions of real-time data points covering speed of serve, forced and unforced errors, and even the social media sentiment surrounding each game. This data can then be turned into automated stories or Twitter TWTR -2.13% messages to ensure Wimbledon are the first to break news stories about the results.

Already journalists have expressed worries that technology like that could put them out of a job. But the truth is, if it is possible to teach the process of structuring data into a narrative to a human, it can be taught to a computer too.

Kris Hammond, co-founder and chief scientist atNarrative Science, which has created the Quill natural language generation platform, realized early on that technology could be used to turn information into easy to understand narratives. In fact, Quill is a regular contributor to Forbes–just like me. You can see its latest contributions here.

Quill, or competing apps like Automated Insights are used by other media outlets – but due to a lack of information over how trustworthy readers would consider reports created by algorithms, many news publishers may be reluctant to admit whether their stories, or parts of them, are generated by computers.

US Federal Reserve Chairman reads the Financial Times. AFP PHOTO / Jim WATSON (Photo credit should read JIM WATSON/AFP/Getty Images)
The implications of this technology go further than putting journalists out of work, however. In fact Hammond concedes Quill isn’t yet great at finding news stories–its strengths lie in putting stories together from specific data sources. Narrative Science is currently running one application which reads the stock market and attempts to spot when unusual highs, lows or volume spikes could have important implications, but Hammond calls this a “very controlled” instance of Quill digging up its own stories. He stands by his claim, made in 2012 that a computer would be able to write Pulitzer-prize quality journalism within five years–although he admits the clock is ticking!

How does this apply to medicine and health care?  Many of the articles we read are culled and selected by Quill for publication in newspapers or relevant internet and/or print publications.
No, the real value, Hammond says, is not in the scattershot approach of news publishing, where one article is created for a vast audience in the hope that some will find it interesting or useful. Natural language generation and automated narrative creation mean that one dataset can be interpreted in multiple ways, giving each targeted audience segment precisely what they need to know, without any confusing background noise.
Narratives are one of the most important tools we have. Humans have always told stories – fictional, real or somewhere in between–as a way of passing on information and influencing events. Giving that power to computers may, to some, seem a step too far. But don’t we already often distrust the concept of “narrative”? The word is commonly used interchangeably with “spin” to suggest that someone is tailoring their depiction of events to suit their own needs. Computers can’t “spin” (unless they are programmed to, of course) so for news reporting, or conveying hard facts about a business, couldn’t they be seen as more trustworthy than humans?

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